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    Coursera
    1. Convolutional Neural Networks
    2. Week 2
    3. Deep Convolutional Models
    PreviousNext
      • Completed
        Quiz: Deep Convolutional Models
        10 questions
    QuizQuiz • 30 min30 minutes

    Deep Convolutional Models

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    Deep Convolutional Models
    Graded Quiz • 30 min

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    Deep Convolutional Models

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    1.
    Question 1

    Which of the following do you typically see in a ConvNet? (Check all that apply.)

    1 / 1 point
    Check
    Correct

    True, fully-connected layers are often used after flattening a volume to output a set of classes in classification.

    Check
    Correct

    True, as seen in the case studies.

    2.
    Question 2

    In order to be able to build very deep networks, we usually only use pooling layers to downsize the height/width of the activation volumes while convolutions are used with “valid” padding. Otherwise, we would downsize the input of the model too quickly.

    1 / 1 point
    Check
    Correct

    Correct!

    3.
    Question 3

    Training a deeper network (for example, adding additional layers to the network) allows the network to fit more complex functions and thus almost always results in lower training error. For this question, assume we’re referring to “plain” networks.

    1 / 1 point
    Check
    Correct

    Correct, Resnets are here to help us train very deep neural networks.

    4.
    Question 4

    The following equation captures the computation in a ResNet block. What goes into the two blanks above?

    a[l+2]=g(W[l+2]g(W[l+1]a[l]+b[l+1])+bl+2+_______ )+_______

    1 / 1 point
    Check
    Correct

    Correct

    5.
    Question 5

    Which ones of the following statements on Residual Networks are true? (Check all that apply.)

    1 / 1 point
    Check
    Correct

    This is true.

    Check
    Correct

    This is true.

    6.
    Question 6

    Suppose you have an input volume of dimension nHn_HnH​ x nWn_WnW​ x nCn_CnC​. Which of the following statements you agree with? (Assume that “1x1 convolutional layer” below always uses a stride of 1 and no padding.)

    1 / 1 point
    Check
    Correct

    This is correct.

    Check
    Correct

    Yes, a 1x1 convolutional layer with a small number of filters is going to reduce nCn_CnC​ but will keep the dimensions nHn_HnH​ and nWn_WnW​

    7.
    Question 7

    Which ones of the following statements on Inception Networks are true? (Check all that apply.)

    1 / 1 point
    Check
    Correct
    Check
    Correct
    8.
    Question 8

    Which of the following are common reasons for using open-source implementations of ConvNets (both the model and/or weights)? Check all that apply.

    1 / 1 point
    Check
    Correct

    True

    Check
    Correct

    True

    9.
    Question 9

    In Depthwise Separable Convolution you:

    1 / 1 point
    Check
    Correct
    Check
    Correct
    Check
    Correct
    Check
    Correct
    10.
    Question 10

    Fill in the missing dimensions shown in the image below (marked W, Y, Z).

    1 / 1 point
    Check
    Correct
    ˆ